Network embedding literature can take two-fold. The first method is using matrix factorization techniques. With this method, the entire graph is represented as an adjacency matrix where each cell in the matrix represent node relationship. This matrix is processed and projected into a lower dimensional space [1]}. This method comes with a cost of lower scalability and it does not capture structural properties of a node in the network. Neural network based network embedding can overcome the above mentioned limitations of matrix factorization approach. Nodes in the network can be organized in such a way that the neural network based Skip-gram model can be applied to learn features by optimizing neighborhood preserving objective. The most efficient method to gather network neighborhood is to do random walks [2]}. The two most popular methods that uses random walks and Skip-gram model to learn features of large-scale networks are DeepWalk [3]} and Node2Vec [4]}.
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